World Library  
Flag as Inappropriate
Email this Article

Probabilistic forecasting

Article Id: WHEBN0004103640
Reproduction Date:

Title: Probabilistic forecasting  
Author: World Heritage Encyclopedia
Language: English
Subject: Scoring rule, Statistical forecasting, Upper-atmospheric models, Weather forecasting, Drought in Canada
Publisher: World Heritage Encyclopedia

Probabilistic forecasting

Probabilistic forecasting summarises what is known, or opinions about, future events. In contrast to a single-valued forecasts (such as forecasting that the maximum temperature at given site on a given day will be 23 degrees Celsius or that the result in a given football match will be a no-score draw), probabilistic forecasts assign a probability to each of a number of different outcomes, and the complete set of probabilities represents a probability forecast.

Weather forecasting represents a service in which probability forecasts are sometimes published for public consumption, although they may also be used by weather forecasters as the basis of a simpler type of forecast. For example forecasters may combine their own experience together with computer-generated probability forecasts to construct a forecast of the type "we expect heavy rainfall".

Sports betting is another field of application where probabilistic forecasting can play a role. The pre-race odds published for a horse race can be considered to correspond to a summary of bettors' opinions about the likely outcome of a race, although this needs to be tempered with caution as bookmakers' profits needs to be taken into account. In sports betting, probability forecasts may not be published as such, but may underlie bookmakers' activities in setting pay-off rates, etc..

Weather forecasting

Probabilistic forecasting is used in a weather forecasting in a number of ways. One of the simplest is the publication of about rainfall in the form of a probability of precipitation.


The probability information is typically derived by using several numerical model runs, with slightly varying initial conditions. This technique is usually referred to as ensemble forecasting by an Ensemble Prediction System (EPS). EPS does not produce a full forecast probability distribution over all possible events, and it is possible to use purely statistical or hybrid statistical/numerical methods to do this.[1] For example, temperature can take on a theoretically infinite number of possible values (events); a statistical method would produce a distribution assigning a probability value to every possible temperature. Implausibly high or low temperatures would then have close to zero probability values.

If it were possible to run the model for every possible set of initial conditions, each with an associated probability, then according to how many members (i.e., individual model runs) of the ensemble predict a certain event, one could compute the actual conditional probability of the given event. In practice, forecasters try to guess a small number of perturbations (usually around 20) that they deem are most likely to yield distinct weather outcomes. Two common techniques for this purpose are breeding vectors (BV) and singular vectors (SV).[2] This technique is not guaranteed to yield an ensemble distribution identical to the actual forecast distribution, but attaining such probabilistic information is one goal of the choice of initial perturbations. Other variants of ensemble forecasting systems that have no immediate probabilistic interpretation include those that assemble the forecasts produced by different numerical weather prediction systems.


Canada has been one of the first countries to broadcast their probabilistic forecast by giving chances of precipitation in percentages. As an example of fully probabilistic forecasts, recently, distribution forecasts of rainfall amounts by purely statistical methods have been developed whose performance is competitive with hybrid EPS/statistical rainfall forecasts of daily rainfall amounts.[3]

Energy usage

Lumina Decision Systems has created an example probabilistic forecast of energy usage for the next 25 years using the US Department of Energy's Annual Energy Outlook (AEO) 2010.[4]

Population forecasting

Probability forecasts have also been used in the field of population forecasting.[5]


Assessing probabilistic forecasts is more complex than assessing deterministic forecasts.[6] If an ensemble-based approach is being used, the individual ensemble members need first to be combined and expressed in terms of a probability distribution.[7] There exist probabilistic (proper) scoring rules such as the continuous-ranked probability score for evaluating probabilistic forecasts.[8] One example of such a rule is the Brier score.

See also


  1. ^ Wilks, D.S. (2005), Statistical Methods in the Atmospheric Sciences, Second Edition. (International geophysics series, Volume 91). Academic Press. ISBN 0-12-751966-1
  2. ^ Toth, Z. and Kalnay, E. (1997), "Ensemble Forecasting at NCEP and the Breeding Method", Monthly Weather Review, 125, pp. 3298.
  3. ^ Little, M.A. et al. (2009), "Generalized Linear Models for Site-Specific Density Forecasting of UK Daily Rainfall". Monthly Weather Review, 37(3), 1029–1045
  4. ^ Lumina Decision Systems 2010, "Probabilistic Forecast Libraries" (see menu under "Services").
  5. ^ Wilson, T.; Bell, M. (2007). "Probabilistic Regional Population Forecasts: The Example of Queensland, Australia". Geographical Analysis 39: 1.  
  6. ^ Jolliffe, I.T., Stephenson, D.B. (2003) Forecast Verification: A Practitioner's Guide in Atmospheric Science. Wiley. ISBN 0-471-49759-2
  7. ^ Schölzel, C., A. Hense (2011): Probabilistic assessment of regional climate change in Southwest Germany by ensemble dressing, Climate Dynamics 36 (9), 2003-2014
  8. ^ Gneiting, T. and Raftery, A.E. (2007), "Strictly Proper Scoring Rules, Prediction, and Estimation". Journal of the American Statistical Association, 102, pp. 359–378

External links

  • Online results from EPS (from the World Meteorological Organisation)
This article was sourced from Creative Commons Attribution-ShareAlike License; additional terms may apply. World Heritage Encyclopedia content is assembled from numerous content providers, Open Access Publishing, and in compliance with The Fair Access to Science and Technology Research Act (FASTR), Wikimedia Foundation, Inc., Public Library of Science, The Encyclopedia of Life, Open Book Publishers (OBP), PubMed, U.S. National Library of Medicine, National Center for Biotechnology Information, U.S. National Library of Medicine, National Institutes of Health (NIH), U.S. Department of Health & Human Services, and, which sources content from all federal, state, local, tribal, and territorial government publication portals (.gov, .mil, .edu). Funding for and content contributors is made possible from the U.S. Congress, E-Government Act of 2002.
Crowd sourced content that is contributed to World Heritage Encyclopedia is peer reviewed and edited by our editorial staff to ensure quality scholarly research articles.
By using this site, you agree to the Terms of Use and Privacy Policy. World Heritage Encyclopedia™ is a registered trademark of the World Public Library Association, a non-profit organization.

Copyright © World Library Foundation. All rights reserved. eBooks from Project Gutenberg are sponsored by the World Library Foundation,
a 501c(4) Member's Support Non-Profit Organization, and is NOT affiliated with any governmental agency or department.